Biomarkers, Pathways and Personalized Therapy of Lung Cancer
Pan-Chyr Yang, MD, PhD
National Taiwan University College of Medicine

More than 85% of the lung cancers are NSCLC and 75% of which are diagnosed at advanced stages. The 5-year survival rate of NSCLC is approximately 15%. Early diagnosis, precise risk assessment, optimized therapy selections, proper treatment response monitoring and early recurrence detection are five key issues in the new-era of lung cancer management. Controversies in low-dose CT screening program and pitfalls in TMN staging system strengthen the necessity of molecular tools development to improve the treatment outcome of lung cancer patients.
Recent advances in genomics, epigenomics, proteomics and molecular pathology have identified many potential biomarkers for clinical applications. Integration of biomarkers into clinical practice provides possibilities of early lung cancer detection and personalized therapy. The biomarker can be classified into three types. The prognostic markers are biomarkers that can be used to estimate patients’ outcome independent of therapeutic decision. The predictive markers are biomarkers that can help to make therapeutic decision for the patients. The diagnostic biomarkers are biomarkers that can assist disease diagnosis, disease classification and monitoring of the therapeutic response. EGFR, ERCC1, RRM1, and KRAS are four currently recognized prognostic and predictive biomarkers in NSCLC. The recent IPASS study confirmed that patients with active EGFR mutations had better treatment response and progression free survival to tyrosine kinase inhibitors (TKIs) therapy. The ERCC1 expression predicts cisplatin resistance and RRM1 predicts poor responses to gemcitabine. KRAS mutation is a poor prognostic factor and it predicts resistance to chemotherapy and TKI therapy.
 Lung cancer is a heterogeneous disease. Current evidences suggest the possibility of different disease pathogenesis among Eastern and Western populations. With the help of high-throughput genomic technologies and bioinformatics, researchers are now able to do large scale datasets comparison and pathway analysis based on gene expressions and proteomic profiles. In conjunction with the pathway analysis softwares (ONCOMINE, INGENUITY, BioCarta, MetaCore, KEGG, CRSD etc), we are able to identify pathways involved in the cancer progression. The identification of new pathogenic pathways facilitates therapeutic targets exploration and specific drugs discovery. This approach may potentiate the dream of personalized, molecularly-tailored therapy for lung cancer patients come true in the near future.